Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed that utilizes machine learning models for patch retrieval and deformation in completing three-dimensional digital shapes. In particular, in one or more implementations the disclosed systems utilize a machine learning model to predict a coarse completion shape from an incomplete 3D digital shape. The disclosed systems sample coarse 3D patches from the coarse 3D digital shape and learn a shape distance function to retrieve detailed 3D shape patches in the input shape. Moreover, the disclosed systems learn a deformation for each retrieved patch and blending weights to integrate the retrieved patches into a continuous surface.

Abstract: Certain aspects and features of this disclosure relate to neural network based 3D object surface mapping. In one example, a first representation of a first surface of a first 3D object and a second representation of a second surface of a second 3D object are produced. A surface mapping function is generated for mapping the first surface to the second surface. The surface mapping function is defined the representations and by a neural network model configured to map a first 2D representation of the first surface to a second 2D representation of the second surface. One or more features of the a first 3D mesh on the first surface can be applied to a second 3D mesh on the second surface using the surface mapping function to produce a modified second surface, which can be rendered through a user interface.

Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

Abstract: Embodiments are disclosed for generating 360-degree panoramas from input narrow field of view images. A method of generating 360-degree panoramas may include obtaining an input image and guide, generating a panoramic projection of the input image, and generating, by a panorama generator, a 360-degree panorama based on the panoramic projection and the guide, wherein the panorama generator is a guided co-modulation generator network trained to generate a 360-degree panorama from the input image based on the guide.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for generating digital chain pull paintings in digital images. The disclosed system digitally animates a chain pull painting from a digital drawing path by determining a plurality of digital bead points along the digital drawing path. In response to a movement of one of the digital bead points from a first position to a second position (e.g., based on a pull input performed at a selected digital bead point), the disclosed system determines updated positions of one or more digital bead points along the path. The disclosed system also generates one or more strokes in the digital image from previous positions of the digital bead points to the updated positions of the digital bead points.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

Abstract: Embodiments of the present invention are directed towards intuitive editing of three-dimensional models. In embodiments, salient geometric features associated with a three-dimensional model defining an object are identified. Thereafter, feature attributes associated with the salient geometric features are identified. A feature set including a plurality of salient geometric features related to one another is generated based on the determined feature attributes (e.g., properties, relationships, distances). An editing handle can then be generated and displayed for the feature set enabling each of the salient geometric features within the feature set to be edited in accordance with a manipulation of the editing handle. The editing handle can be displayed in association with one of the salient geometric features of the feature set.

Abstract: Certain aspects and features of this disclosure relate to neural network based 3D object surface mapping. In one example, a first representation of a first surface of a first 3D object and a second representation of a second surface of a second 3D object are produced. A surface mapping function is generated for mapping the first surface to the second surface. The surface mapping function is defined the representations and by a neural network model configured to map a first 2D representation of the first surface to a second 2D representation of the second surface. One or more features of the a first 3D mesh on the first surface can be applied to a second 3D mesh on the second surface using the surface mapping function to produce a modified second surface, which can be rendered through a user interface.

Abstract: Systems and methods for performing image search are described. An image search method may include generating a feature vector for each of a plurality of stored images using a machine learning model trained using a rotation loss term, receiving a search query comprising a search image with object having an orientation, generating a query feature vector for the search image using the machine learning model, wherein the query feature vector is based at least in part on the orientation, comparing the query feature vector to the feature vector for each of the plurality of stored images, and selecting at least one stored image of the plurality of stored images based on the comparison, wherein the at least one stored image comprises a similar orientation to the orientation of the object in the search image.

Abstract: Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.

Abstract: Certain embodiments involve techniques for generating a 3D representation based on a provided 2D image of an object. An image generation system receives the 2D image representation and generates a multi-dimensional vector of the input that represents the image. The image generation system samples a set of points and provides the set of points and the multi-dimensional vector to a neural network that was trained to predict a 3D surface representing the image such that the 3D surface is consistent with a 3D surface of the object calculated using an implicit function for representing the image. The neural network predicts, based on the multi-dimensional vector and the set of points, the 3D surface representing the object.

Abstract: Embodiments provide systems, methods, and computer storage media for generating a 3D model from a target 2D image or 3D point cloud (e.g., generated by a 3D scan). Given a particular target, a retrieval network retrieves or identifies a source model from a database, and a deformation network deforms the source model to fit the target. In some cases, joint learning is employed to enable the retrieval and deformation networks to jointly learn a deformation-aware retrieval embedding space and an individualized deformation space for each source model. In some cases, the retrieval network retrieves based on distance in the deformation-aware retrieval embedding space, enabling the retrieval module to retrieve a source model that best fits to the target after deformation. In some cases, a deformation is decomposed into a plurality of per-part deformations, and/or and the retrieval embedding space is used to select training data.

Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

Abstract: Using a prediction engine, generating, based on deformations of prior editing operations performed with a graphics editing tool, suggested editing operations that augment current editing operations applied to a graphical object. The prediction engine accesses first samples defining first positions along first paths of previous editing operations applied to a mesh object in a previous frame and second samples defining second positions along second paths of executed editing operations applied in a current frame. The prediction engine identifies, from a comparison of the first samples and the second samples, a matching component set from the previous editing operations that corresponds to the executed editing operations. The prediction engine deforms the first samples toward the second samples and determines suggested editing operations that comprise a non-matching component set as modified based on the deformed first samples.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for reconstructing three-dimensional meshes from two-dimensional images of objects with automatic coordinate system alignment. For example, the disclosed system can generate feature vectors for a plurality of images having different views of an object. The disclosed system can process the feature vectors to generate coordinate-aligned feature vectors aligned with a coordinate system associated with an image. The disclosed system can generate a combined feature vector from the feature vectors aligned to the coordinate system. Additionally, the disclosed system can then generate a three-dimensional mesh representing the object from the combined feature vector.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for iteratively decimating a three-dimensional mesh utilizing successive self-parameterization. For example, the disclosed system can self-parameterize local geometries of a three-dimensional mesh using surface mappings within a two-dimensional surface mapping space. The disclosed system can collapse edges in the three-dimensional mesh to create new vertices from the collapsed edges. The disclosed system can parameterize the collapsed edges based on the surface mappings to collapse corresponding edges within the surface mapping space. The disclosed system can thus generate a decimated three-dimensional mesh by collapsing edges in the three-dimensional mesh while providing a bijective map between points in the decimated three-dimensional mesh and corresponding points in the three-dimensional mesh.

Abstract: Techniques are disclosed for 3D object reconstruction using photometric mesh representations. A decoder is pretrained to transform points sampled from 2D patches of representative objects into 3D polygonal meshes. An image frame of the object is fed into an encoder to get an initial latent code vector. For each frame and camera pair from the sequence, a polygonal mesh is rendered at the given viewpoints. The mesh is optimized by creating a virtual viewpoint, rasterized to obtain a depth map. The 3D mesh projections are aligned by projecting the coordinates corresponding to the polygonal face vertices of the rasterized mesh to both selected viewpoints. The photometric error is determined from RGB pixel intensities sampled from both frames. Gradients from the photometric error are backpropagated into the vertices of the assigned polygonal indices by relating the barycentric coordinates of each image to update the latent code vector.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing one or more neural networks to recursively subdivide a three-dimensional mesh according to local geometries of vertices in the three-dimensional mesh. For example, the disclosed system can determine a local geometry (e.g., a one-ring neighborhood of half-flaps) for each vertex in a three-dimensional mesh. For each subdivision iteration, the disclosed system can then utilize a neural network to determine displacement coordinates for existing vertices in the three-dimensional mesh and coordinates for new vertices added to edges between the existing vertices in the three-dimensional mesh in accordance with the local geometries of the existing vertices. Furthermore, the disclosed system can generate a subdivided three-dimensional mesh based on the determined displacement coordinates for the existing vertices and the determined coordinates for the new vertices.